Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Building J12, Sydney, NSW, 2006, Australia,
J Digit Imaging. 2013 Dec;26(6):1025-39. doi: 10.1007/s10278-013-9619-2.
Medical imaging is fundamental to modern healthcare, and its widespread use has resulted in the creation of image databases, as well as picture archiving and communication systems. These repositories now contain images from a diverse range of modalities, multidimensional (three-dimensional or time-varying) images, as well as co-aligned multimodality images. These image collections offer the opportunity for evidence-based diagnosis, teaching, and research; for these applications, there is a requirement for appropriate methods to search the collections for images that have characteristics similar to the case(s) of interest. Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search criteria. Medical CBIR is an established field of study that is beginning to realize promise when applied to multidimensional and multimodality medical data. In this paper, we present a review of state-of-the-art medical CBIR approaches in five main categories: two-dimensional image retrieval, retrieval of images with three or more dimensions, the use of nonimage data to enhance the retrieval, multimodality image retrieval, and retrieval from diverse datasets. We use these categories as a framework for discussing the state of the art, focusing on the characteristics and modalities of the information used during medical image retrieval.
医学成像在现代医疗保健中至关重要，其广泛应用导致了图像数据库的创建，以及图像存档和通信系统的出现。这些存储库现在包含了来自各种模态的图像、多维（三维或时变）图像以及对齐的多模态图像。这些图像集合为基于证据的诊断、教学和研究提供了机会；对于这些应用，需要有适当的方法来搜索具有与感兴趣病例相似特征的图像。基于内容的图像检索（CBIR）是一种图像搜索技术，它通过使用视觉特征（如颜色、纹理和形状）作为搜索标准，补充了传统的基于文本的图像检索。医学 CBIR 是一个已建立的研究领域，当应用于多维和多模态医学数据时，开始展现出其潜力。在本文中，我们将回顾五个主要类别的最先进的医学 CBIR 方法：二维图像检索、三维或更多维图像的检索、使用非图像数据增强检索、多模态图像检索以及从不同数据集进行检索。我们使用这些类别作为讨论现状的框架，重点关注在医学图像检索过程中使用的信息的特征和模态。
